genomics and drug development - ucl · director, ucl institute of cardiovascular science professor...

Aroon HingoraniDirector, UCL Institute of Cardiovascular ScienceProfessor of Genetic EpidemiologyUniversity College [email protected]

UCL Hospitals National Institute of Health ResearchBiomedical Research Centre

Genomics and drug development

Hingorani A D et al. BMJ 2010;341:bmj.c5945

©2010 by British Medical Journal Publishing Group

Human Genetic Variation

Genetic spectrum of human disease

Monogenic disorder

Mutation

Polymorphism

Disease

X Y

X Y Other genes

Environment

DiseaseHealth

SinglenucleotidePolymorphism(SNP)

XY

Other genes

Environment

DiseaseHealth

Polygenic disorder

Genomics and the Whitehall II study

• Creation of a DNA biorepository

• Targeted genotyping

• Cardiochip - 50,000 variants

• Metabochip – 200,000 variants imputed to 2M

• DrugDev Array – 480,000 variants imputed to ?

• The UCLEB Consortium

University College-London School-Edinburgh-Bristol (UCLEB) Consortium

Phenoytpes: wide coverage of organs/systems allows efficacy and safety profiling for most commondisorders

> 52 blood markers in up to 27,000 samples

~ 216 NMR metabolomic traits in >11,000 samples and leveraged funding for additional 15,000 samples

Organ/System Phenotype Associated disease outcome

Brain: Cognitive function Alzheimer’s disease

Heart: ECG traits AF, sudden death

Blood: Ultra-dense lipids Type-2 diabetes

Blood vessel: Carotid atherosclerosis Atherosclerotic vascular disease

Lung: FEV1, FVC COPD

Kidney: eGFR End-stage renal disease

Liver: AST, ALA, GGT Fatty liver and chirrosis

Bone: Bone mineral density Osteoporosis

Clinical events

>5000 CVD events including CHD and stroke.

Additional CV events: angina, heart failure, and DVT/PE

>4000 cancer events

>2000 Type-2 diabetes cases

~2000 COPD cases

Genomics and Drug Development -Overview

• Process of drug development and the potentialbenefits of genomic support for drug targetselection and validation

• The Druggable Genome

• Design of a new genotyping array to support drugdevelopment

Pre-clinical development Clinical development

Late-stage failure

Programme attritionCost

Drug development process

After:Kola & Landis, Nature Reviews Drug Discovery 2004; 3, 711-716Arrowsmith, Nature Reviews Drug Discovery 2011; 10, 328-329; andNature Reviews Drug Discovery 2013; 12, 569PaulS et al. Nature Reviews Drug Discovery 2010; 9, 203-214

Pre-clinical development Clinical development

Late-stage failure

Poor predictive accuracyof preclinical studies

Definitive target validationexperiment (the phase III RCT)

is the final step

Drug development process

Randomised controlled trial (RCT; Phase III)

Patients

Randomisation

Intervention Placebo

Target affected Target unaffected

Outcome Outcome

The RCT is the pivotal drug target validation experiment

Design feature Attribute

In humans Avoids limitations ofexperiments in cells,isolated organs and animalmodels

Randomised experimentalintervention

Overcomes confoundingand reverse causationinherent in humanobservational studies

Pre-specified efficacy andsafety outcomes, carefulsample size determination

Low risk of false positivefindings

RCT (Phase III)

Patients

Randomisation

Genetic studies as Nature’s randomised trials

Mendelian randomisation trial

Population

Random allocation of alleles

Target genotype aa Target genotype AA

Target activityunchanged

Outcome Outcome

Target expression or activitymodified

Variants of a gene encoding a drug target, allocated at random at conception,that affectits expression or function,

can be used as a tool to infer the outcome of modifying the same target pharmacologically

Hingorani A, Humphries S. Lancet 2005; 1906–1908

Intervention Placebo

Target affected Target unaffected

Outcome Outcome

Target protein

Intendedoutcome

On-target effect

Encodinggene

Compound

Relationship between gene, target and compound

Target protein

Intendedoutcome

On-target effect

HMGCR

Relationship between gene, target and compound

Statin

HMG-coA reductase

RCT (Phase III)

Sample

Randomisation

HMG-CoA red inhibitor Placebo

LDL-C reduced LDL-C unchanged

CV eventrate lower

CV eventrate higher

Protein target: HMGCR

Off target

Mendelian randomisation Trial

Population

Random allocation of alleles

HMGCR aa Genotype AA

LDL-C unchanged

CV eventrate lower

CV eventrate higher

LDL-C reduced

Protein target: HMGCR

HMGCR variant (rs12916)

LDL-C reduced by 0.07 mmol/L

CHD risk reduction 6%.

Ference et al. J Am Coll Cardiol 2012; 60(25):2631-9

HMGCR inhibitors (statins)

LDL-C reduced 1 mmol/L

CHD risk reduction 25%

CTT Lancet 2010, 376, 1670–1681

HMGCR variants, statins, LDL-C and coronary events

Common genetic variants and small phenotypic effectsize

0.06 mmol/Lper allele

Courtesy Daniel Swerdlow

Genomic support for drug target selectionand validation: selected examples

Pre-clinical development

MR trials: Example 1 –PLA2G2A, sPLA2, varespladib and

CVD events

Clinical trials

Progression of a new therapeutic at acritical decision point

sPLA2, Varespladib and VascularEvents: Phase-III trial (JACC 2013)

Holmes MV et al. JACC 2013 Nov 19;62(21):1966-76

Summary findings pre Phase-III trial: sPLA2-IIA concentration and activity is associatedwith incident and recurrent major vascular events

Drug-target and therapeutic: a small molecule sPLA2 inhibitor (Varespladib; Anthera)reduced sPLA2 mass by ~90%

VISTA-16: Randomised 5145 patients with acute coronary syndrome to varespladib500mg daily or placebo. Outcome was assessed at 16 weeks. The trial was stopped atprespecified interim analysis for futility or possible harm.

Summary results (http://www.anthera.com/VISTA-16.pdf)

HR for primary outcome (CVD death, non-fatal MI, stroke): 1.24, p=0.155HR for stroke 1.43, p=0.025

HR for non-fatal MI 1.68, p=0.009

Varespladib and Cardiovascular Events in Patients With an Acute Coronary Syndrome

JAMA. 2014;311(3):252-262. doi:10.1001/jama.2013.282836

General Population: Incident eventsMajor vascular events

Nonfatal MINonfatal StrokeFatal MI/Stroke

General Population: Prevalent eventsMajor vascular events

MIStroke

Acute Coronary Syndrome: Recurrent eventsMajor vascular events

Nonfatal MINonfatal StrokeFatal MI/Stroke †

OutcomeSetting,

13 (8021/56359)13 (4208/51016)11 (2304/46790)12 (1509/48118)

12 (7513/55523)12 (6411/54884)8 (1102/37280)

9 (2520/15768)8 (1158/14152)6 (223/12283)9 (1139/15724)

(events/participants)Studies

1.02 (0.98, 1.06)1.04 (0.98, 1.10)1.00 (0.93, 1.07)1.01 (0.93, 1.10)

0.99 (0.95, 1.03)0.98 (0.93, 1.03)1.03 (0.93, 1.15)

0.96 (0.90, 1.03)0.99 (0.89, 1.09)0.85 (0.69, 1.06)0.96 (0.87, 1.06)

allele) (95% CI)Odds ratio (per

26(0,51)22(0,59)19(0,59)41(0,70)

38(0,63)52(7,75)0(0,67)

0(0,45)28(0,67)0(0,74)0(0,64)

(95%CI)I2,%

1.02 (0.98, 1.06)1.04 (0.98, 1.10)1.00 (0.93, 1.07)1.01 (0.93, 1.10)

0.99 (0.95, 1.03)0.98 (0.93, 1.03)1.03 (0.93, 1.15)

0.96 (0.90, 1.03)0.99 (0.89, 1.09)0.85 (0.69, 1.06)0.96 (0.87, 1.06)

allele) (95% CI)Odds ratio (per

26(0,51)22(0,59)19(0,59)41(0,70)

38(0,63)52(7,75)0(0,67)

0(0,45)28(0,67)0(0,74)0(0,64)

(95%CI)I2,%

Lower Higher

1.5 1 2

Odds ratio

Association between PLA2G2A rs11573156and CVD outcomes (per C allele)

Holmes MV et al. J. Am Coll Cardiol 2013 Nov 19;62(21):1966-76

MR Trials: Example 1 - PLA2G2A rs11573156 allele and CVD outcomes

Target protein

Compound

Intendedoutcome

On-target effectOff-target effect

Unintendedoutcome

Unintendedoutcome

Other protein

Encodinggene

MR Trials – distinguishing on from off-target effects

Target protein

Compound

Intendedoutcome

On-target effectOff-target effect

Unintendedoutcome

Unintendedoutcome

Other protein

Encodinggene

Target profile

Compound profile

MR Trials – distinguishing on from off-target effects

Drug Randomisation

TRGHDL

No change in lipids

Sample

Torcetrapib Control

CETP-inhibition No-CETP inhibition

LDL

Change in lipid traits

BP(Off-target)?

Trait RCTs(individuals)

Torcetrapib/atorvastatinvs

Atorvastatin alone

Mean difference (95%CI)

HDL-C (mmol/L) 17911 0.78 (0.68, 0.87)

Systolic BP(mmHg)

17911 4.471(4.09, 4.84)

Hazard ratio (95%CI)

CVD events 15067 1.25 (1.09,1.44)

BP(On-target)?

Sofat R et al. Circulation 2010 Jan 5;121(1):52-62

MR trials:Example 2 – CETP, torcetrapib,HDL-C and BP

CETP gene variants, lipids and BP

2010 Jan 5;121(1):52-62

CETP Lipids and apolipoproteins

Blood pressure

Sofat R et al. Circulation 2010 Jan 5;121(1):52-62

The BP raising effect oftorcetrapib is off-target

MR trials: Example 3 – potential repurposing

Clinical trialsPre-clinical development

RepurposingIL6R blockade (tocilizumab)

and CHD(Lancet 2012)

Inflammation strongly linked to CHD butno currently validated therapeutic target

Clinical trialsPre-clinical development

Rheumatoid arthritis

CHD

Drug interventionPatients with rheumatoid arthritis

Randomisation (Tocilizumab)

IL6R- blocker (MAB) Placebo

Reduced IL6 signalling IL6 signalling unchanged

RA diseaseActivity lower

RA diseaseactivity higher

Protein target: IL6R

Biomarker Tocilizumab

IL-6 (n=1,446)

CRP (n=3,010)

Fibrinogen (n=409)Soluble IL-6R (n=1,465)

Albumin (n=108)

Haemoglobin (n=2,072)

Repurposing IL6R as a target for CHDThe Interleukin-6 Receptor Mendelian Randomisation Analysis(IL6R MR) Consortium* Lancet 2012; 379: 1214–24

People at risk of CHD

Random allocation of IL6R alleles

IL6R aa IL6R AA

IL6 signalling unchanged

CV eventrate lower

CV eventrate higher

Genetic study: natural randomisation

Reduced IL6 signalling


Drug interventionPatients with rheumatoid arthritis



Reduced IL6 signalling IL6 signalling unchanged




Biomarker Tocilizumab

IL-6 (n=1,446)

CRP (n=3,010)

Fibrinogen (n=409)Soluble IL-6R (n=1,465)

Albumin (n=108)

Haemoglobin (n=2,072)

Repurposing IL6R as a target for CHD

Drug intervention


Reduce IL6 signalling IL6 signalling unchanged



Biomarker Tocilizumab IL6R SNP rs7529229

IL-6 (n=1,446) (n=29,838)

CRP (n=3,010) (n=76,527)

Fibrinogen (n=409) (n=52,667)Soluble IL-6R (n=1,465) (n=1,454)

Albumin (n=108) (n=5,787)

Haemoglobin (n=2,072) (n=17,898)

Patients with rheumatoid arthritis



Genetic study: natural randomisation

Repurposing IL6R as a target for CHD

Additional examples• Darapladib, LpPLA2 and CHD

MR trial: Casas JP. et al. Circulation 2010 Jun 1;121(21):2284-93RCT: STABILITY N Engl J Med 2014 May 1;370(18):1702-11;SOLID TIMI 52 JAMA. 2014 Sep 10;312(10):1006-15

• Folic acid, homocysteine and stroke

MR trial: Holmes MV et al. Lancet 2011 Aug 13;378(9791):584-94RCT: Huo et al. JAMA 2015 Apr 7;313(13):1325-35

• Ezetimibe, LDL-C and CHD

MR trial: MI Genetics Consortium Investigators N Engl J Med 2014 Nov 27;371(22):2072-82RCT: Cannon CP et al. N Engl J Med 2015 Jun 18;372(25):2387-97

Published Genome-Wide Associations through12/2013

http://www.ebi.ac.uk/gwas/

GWAS ‘rediscoveries’ of human drug targetsGWAS Phenotype Associated Gene (Ensembl ID) Associated Gene Description Compound USAN/INN

Total/LDL cholesterolHMGCR(ENSG00000113161)

3-hydroxy-3-methylglutaryl-CoAreductase

Lovastatin,Pravastatin,Simvastatin

Type 2 diabetesKCNJ11(ENSG00000187486)

potassium inwardly-rectifyingchannel subfamily J member 11

Glyburide,Rosiglitazone

PPARG(ENSG00000132170)

peroxisome proliferator-activated receptor gamma

Rosiglitazone,Repaglinide

Nicotine dependenceCHRNA3(ENSG00000080644)

cholinergic receptor, nicotinic,alpha 3

Nicotine,Varenicline

CHRNB4(ENSG00000117971)

cholinergic receptor, nicotinic,beta 4

Nicotine,Varenicline

Courtesy Chris Finan and Felix Kruger

Illumina Human Drug Core – Array Design

Illumina Human Core Array

Whole genome tagSNP markers - 250,421Indel/exome markers >20,000Headroom for custom markers - 200,000

Drug development custom content

Illumina Human Drug Core

~480,179 assays and ~499,367 beadtypes

Targets of approved drugs and thosein clinical development; ADMET (~1426 genes)

Proteins with ‘drug-like compounds orclosely related to drug targets (~682 genes)

Extracellular or transmembrane targetsand members of drug target families(~2370 genes)

Variants of interest: GWAS SNPs; APOE; AIM;fingerprint

Developers: Casas, Finan, Shah, Kruger and Hingorani (UCL); Gaulton and Overington (EBI);together with the Illumina bioinformatics team

Fraction 1kg ph. 3 variantscovered (r2> 0.8)

10

Coverage of druggable genome by genotypingplatforms

Illu DrugDev Consortium 24

Tier 1 Tier 2 Tier 3a Tier 3b

CourtesyDr Chris Finan, UCL

Summary

• Genetic studies in populations share the design features of a randomised controlledtrial (RCT), the pivotal step in drug development

• Alleles in a gene encoding a drug target that affect its expression or activity canhelp predict the effect of modifying the same target pharmacologically

• Genetic studies in populations and patients may help support target selection andvalidation in drug development

Colleagues, collaborators and fundersPhilippa TalmudSteve HumphriesFotios DrenosSonia ShahDelilah Zabaneh

Harry HemingwayMartin BobakAida SanchezEric BrunnerMeena KumariMika KivimakiMichael Marmot

Mike HubankKerra PearceJutta PalmenDavid Balding

Chris PowerElina HyponnenJohn DeanfieldDi KuhAndy WongRichard MorrisPeter Whincup

Jacky Pallas

John WhittakerLiam SmeethFrank DudbridgeClaudio VerzilliLeonelo Bautista

Shah EbrahimDebbie LawlorTom GauntIan DayYoav Ben-ShlomoGeorge Davey SmithJackie PriceGerry Fowkes

Ann RumleyGordon LoweNaveed Sattar

Patsy MunroeToby JohnsonMark Caulfield

Manj SandhuClaudia LangenbergKen OngNick WarehamKay Tee KhawFrances WensleyJohn Danesh

Juan Pablo CasasMeena KumariTina ShahReecha SofatJorgen EngmannDan SwerdlowMichael Holmes

Rosetrees Trust

National Institute forHealth Research

Acknowledgements1. Institute of Cardiovascular Science, and

Farr Institute in London, UniversityCollege London, UK

2. Farr Institute in London, UniversityCollege London, UK

3. European Molecular Biology Laboratory -European Bioinformatics Institute,Cambridge, UK

4. Illumina Inc, San Diego, USA

5. Illumina UK Ltd, Little Chesterford, UK

– Chris Finan1

– Felix Kruger1

– Tina Shah1

– Jorgen Engmann1

– Juan-Pablo Casas1,2

– John Overington1,3

– Anna Gaulton3

– Anneli Karlsson3

– Rita Santos3

– Luana Galver McAuliffe4

– Ryan Kelley4

– Cora Vacher5

Acknowledgements

1. Institute of Cardiovascular Science, andFarr Institute in London, UniversityCollege London, UK

2. Farr Institute in London, UniversityCollege London, UK

3. European Molecular Biology Laboratory -European Bioinformatics Institute,Cambridge, UK

4. Illumina Inc, San Diego, USA

5. Illumina UK Ltd, Little Chesterford, UK

– Aroon Hingorani1

– Chris Finan1

– Felix Kruger1

– Tina Shah1

– Jorgen Engmann1

– Juan-Pablo Casas1,2

– John Overington1,3

– Anna Gaulton3

– Anneli Karlsson3

– Rita Santos3

– Luana Galver McAuliffe4

– Ryan Kelley4

– Cora Vacher5

Extending the use of genetic studiesto support target selection and validationin drug development

• The Druggable genome

• The design of a genotyping array to support target selectionand validation in drug development

The Druggable Genome

• With few exceptions, drug targets are proteins

• Not all proteins are amenable to targeting by the main classes oftherapeutics (small molecule drugs, therapeutic monoclonalantibodies or peptides)

• The ‘druggable genome’: defines the set of genes encoding druggabletargets

• ‘Druggability’ refers to the potential for a protein to be modified by adrug-like small molecule

Prior estimates of the Druggable Genome

• Predated contemporary estimates of the number of protein coding genes

• May not have considered targets of bio-therapeutic drugs (e.g. peptides andand therapeutic monoclonal antibodies)

• May not have included targets of recently licensed first-in-class drugs

An array with custom coverage of thedruggable genome

• Possibility that existing arrays either provided sparse coverageof druggable genes (e.g., GWAS arrays) or dense coverage of amodest number of druggable genes (e.g., gene-centric arrayssuch as metabochip, cardiochip etc)

• Advantage in having dense coverage of known and likely drugtargets across all disease areas

• Allow identification of tractable targets and drug repurposingopportunities

The Illumina Infinium DrugDev ArrayCo-developers: Casas, Finan, Shah, Kruger and Hingorani (UCL); Gaulton and Overington (EBI);together with the Illumina bioinformatics team

Potential users of the array

• Investigators with patient or population samples but noprior genotyping array

• Investigators with patient or population samples previouslygenotyped using a disease-focused fine-mapping array

• Investigators with patient or population samples genotypedusing earlier generation whole genome arrays

• Investigators contemplating genotyping of large-scaleelectronic health record datasets

Sivakumaran et al. Am J Hum Genet. 2011Nov 11; 89(5): 607–618

Pleiotropy in human complex diseases and traits

“……233 (16.9%) genes and 77 (4.6%) SNPs show pleiotropiceffects”

Disease_15 Disease_667 Disease_1123

Gene 1

Gene 20,000

Potential applications of the array

• Drug target discovery - identification of druggable proteins playing a causalrole in a disease of interest

• Drug target validation and prioritisation - informing if and when to advancean existing drug or drug-like compound through a drug-developmentpipeline

• Drug repurposing studies - identifying the role of a drug target in adifferent disease from the current drug indication

• Separating on- vs. off-target effects - for first-in-class and fast followerdrugs

• Stratified medicine studies - within randomised trials or non-randomisedresearch studies

Conclusions

• Genetic studies in populations, case collections and electronic health recorddatasets may help support drug development

• A new array which incorporates GWAS capability with custom content of thedruggable genome as well as genes involved in drug handlingmay help support such studies

• A consortium based on this new array is planned

genomics and drug development - ucl · director, ucl institute of cardiovascular science professor...

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